User generated content (UGC) is becoming more and more popular as digital cameras are now commonplace on a multitude of devices. Users may record a video of themselves or someone else and share it with friends of family via a website or through email.
Synthetic UGC, where video clips are animated by a user, is also increasing in popularity. During elections it is popular to animate avatars representing politicians, and at holidays it is popular to animate characters relating to the holiday, such as elves in relation to Christmas.
Several systems exist that allow a user to animate an avatar through commands. Examples of such commands include commands to make the avatar laugh or smile, show an expression, or move in a particular way. However, a problem with such systems of the prior art is that the avatar can only act based upon a limited set of commands. This often results in a limited ability to express emotion, and accordingly the avatar does not have a life-like appearance.
Certain systems allow for a user to animate an avatar through the user's own movement. Some of these systems include expression detection, wherein a user's expression is detected and then applied to the avatar. Other more advanced systems include modelling of movement and expression of the user, and applying the resulting model to the avatar.
A further problem with these systems of the prior art is that the modelling process can be complex and time consuming. Attempts at reducing complexity can result in a system that is not sufficiently robust and/or wherein the model of the user is not accurate. Similarly, the complex prior art systems are not particularly suited to complexity sensitive applications such as real time and mobile applications.
Yet another problem with systems of the prior art is that the model generated by the systems may not be close to a true representation of the user. This is especially prevalent when a limited amount of training data is provided. In this case, overfitting may occur, wherein the model does not accurately describe the user, yet fits well to the training data.